Cross-scenario transfer learning for estimating mangrove nitrogen and phosphorus content from field hyperspectral data to SDGSAT-1 and Sentinel-2 images

IF 11.1 1区 地球科学 Q1 ENVIRONMENTAL SCIENCES
Bolin Fu , Yan Wu , Li Zhang , Weiwei Sun , Yeqiao Wang , Tengfang Deng , Hongchang He , Keyue Huang
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引用次数: 0

Abstract

Mangroves play a critical role in maintaining biodiversity, supporting global carbon and nitrogen cycles, and contributing to the achievement of the United Nations Sustainable Development Goals (SDGs). Accurate estimation of their nitrogen and phosphorus content is essential for assessing the status of mangrove ecosystems. However, the spectral response characteristics of mangrove leaf nitrogen content (LNC) and leaf phosphorus content (LPC) remain unclear. These knowledge gaps hinder the development of robust predictive models across diverse environmental contexts. To overcome these issues, we collected 375 samples and 16,590 in situ full-spectrum hyperspectral data, and further proposed a novel Global-Fractional Order Sensitivity Analysis (G-FOSA) method. We analyzed for the first time the apparent and deep spectral characteristics of LNC and LPC for four typical mangrove species in China (Avicennia marina, Acanthus ilicifolius, Kandelia candel and Aegiceras corniculatum) using G-FOSA method. This study revealed that the LNC diagnostic wavelengths concentrated in the range of 697 nm–704 nm, while the LPC diagnostic wavelengths were mostly distributed between 691 nm–834 nm and 1869 nm–2236 nm. We developed a mechanism-guided retrieval framework based on these diagnostic wavelengths, and achieved the quantitative inversion from field diagnostic wavelengths to optical satellite (SDGSAT-1 and Sentinel-2) bands. Our experiment results confirmed that SDGSAT-1, the world's first science satellite dedicated to serving the 2030 Agenda for SDGs, performs better in estimating LNC and LPC (R2 = 0.63). Finally, we utilized the advantages of cross-scenario transfer learning technology to design a novel domain adaptive transfer learning (DTL) model, which realized the cross-scenario retrieval of mangrove LNC and LPC across three typical mangrove regions, reducing estimation error (RMSE) by 0.6 %–41.1 % compared to the traditional FTL model. Our work provides new insights and a scientific basis for global mangrove conservation.
从野外高光谱数据到SDGSAT-1和Sentinel-2图像估算红树林氮磷含量的跨场景迁移学习
红树林在维持生物多样性、支持全球碳和氮循环以及促进实现联合国可持续发展目标方面发挥着关键作用。准确估算其氮磷含量对评估红树林生态系统状况至关重要。然而,红树林叶片氮含量(LNC)和叶片磷含量(LPC)的光谱响应特征尚不清楚。这些知识差距阻碍了在不同环境背景下开发强大的预测模型。为了克服这些问题,我们收集了375个样品和16,590个原位全光谱高光谱数据,并进一步提出了一种新的全局分数阶灵敏度分析(G-FOSA)方法。利用G-FOSA方法,首次分析了中国4种典型红树(Avicennia marina、Acanthus ilicifolius、candelia canddel和Aegiceras corniculatum)的LNC和LPC的表观和深光谱特征。结果表明,LNC诊断波长集中在697 nm ~ 704 nm范围内,而LPC诊断波长主要分布在691 nm ~ 834 nm和1869 nm ~ 2236 nm之间。我们基于这些诊断波长开发了一个机制导向的检索框架,并实现了从现场诊断波长到光学卫星(SDGSAT-1和Sentinel-2)波段的定量反演。我们的实验结果证实,世界上第一颗致力于服务于2030年可持续发展目标议程的科学卫星SDGSAT-1在估计LNC和LPC方面表现更好(R2 = 0.63)。最后,利用跨场景迁移学习技术的优势,设计了一种新的领域自适应迁移学习(DTL)模型,该模型实现了红树林LNC和LPC跨三个典型红树林区域的跨场景检索,与传统的FTL模型相比,估计误差(RMSE)降低了0.6% ~ 41.1%。我们的工作为全球红树林保护提供了新的见解和科学基础。
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来源期刊
Remote Sensing of Environment
Remote Sensing of Environment 环境科学-成像科学与照相技术
CiteScore
25.10
自引率
8.90%
发文量
455
审稿时长
53 days
期刊介绍: Remote Sensing of Environment (RSE) serves the Earth observation community by disseminating results on the theory, science, applications, and technology that contribute to advancing the field of remote sensing. With a thoroughly interdisciplinary approach, RSE encompasses terrestrial, oceanic, and atmospheric sensing. The journal emphasizes biophysical and quantitative approaches to remote sensing at local to global scales, covering a diverse range of applications and techniques. RSE serves as a vital platform for the exchange of knowledge and advancements in the dynamic field of remote sensing.
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